---
license: bsd-3-clause
library_name: braindecode
pipeline_tag: feature-extraction
tags:
  - eeg
  - biosignal
  - pytorch
  - neuroscience
  - braindecode
  - convolutional
  - transformer
---

# EEGConformer

EEG Conformer from Song et al (2022) [song2022].

> **Architecture-only repository.** Documents the
> `braindecode.models.EEGConformer` class. **No pretrained weights are
> distributed here.** Instantiate the model and train it on your own
> data.

## Quick start

```bash
pip install braindecode
```

```python
from braindecode.models import EEGConformer

model = EEGConformer(
    n_chans=22,
    sfreq=250,
    input_window_seconds=4.0,
    n_outputs=4,
)
```

The signal-shape arguments above are illustrative defaults — adjust to
match your recording.

## Documentation
- Full API reference: <https://braindecode.org/stable/generated/braindecode.models.EEGConformer.html>
- Interactive browser (live instantiation, parameter counts):
  <https://huggingface.co/spaces/braindecode/model-explorer>
- Source on GitHub: <https://github.com/braindecode/braindecode/blob/master/braindecode/models/eegconformer.py#L14>


## Architecture

![EEGConformer architecture](https://raw.githubusercontent.com/eeyhsong/EEG-Conformer/refs/heads/main/visualization/Fig1.png)


## Parameters

| Parameter | Type | Description |
|---|---|---|
| `n_filters_time: int` | — | Number of temporal filters, defines also embedding size. |
| `filter_time_length: int` | — | Length of the temporal filter. |
| `pool_time_length: int` | — | Length of temporal pooling filter. |
| `pool_time_stride: int` | — | Length of stride between temporal pooling filters. |
| `drop_prob: float` | — | Dropout rate of the convolutional layer. |
| `num_layers: int` | — | Number of self-attention layers. |
| `num_heads: int` | — | Number of attention heads. |
| `att_drop_prob: float` | — | Dropout rate of the self-attention layer. |
| `final_fc_length: int | str` | — | The dimension of the fully connected layer. |
| `return_features: bool` | — | If True, the forward method returns the features before the last classification layer. Defaults to False. |
| `activation: nn.Module` | — | Activation function as parameter. Default is nn.ELU |
| `activation_transfor: nn.Module` | — | Activation function as parameter, applied at the FeedForwardBlock module inside the transformer. Default is nn.GeLU |


## References

1. Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG conformer: Convolutional transformer for EEG decoding and visualization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, pp.710-719. https://ieeexplore.ieee.org/document/9991178
2. Song, Y., Zheng, Q., Liu, B. and Gao, X., 2022. EEG conformer: Convolutional transformer for EEG decoding and visualization. https://github.com/eeyhsong/EEG-Conformer.


## Citation

Cite the original architecture paper (see *References* above) and braindecode:

```bibtex
@article{aristimunha2025braindecode,
  title   = {Braindecode: a deep learning library for raw electrophysiological data},
  author  = {Aristimunha, Bruno and others},
  journal = {Zenodo},
  year    = {2025},
  doi     = {10.5281/zenodo.17699192},
}
```

## License

BSD-3-Clause for the model code (matching braindecode).
Pretraining-derived weights, if you fine-tune from a checkpoint,
inherit the licence of that checkpoint and its training corpus.